基于无线信号循环平稳特征的统计谱域信息传输理论研究

Exploring new degrees of freedom in information transmission is vital to combat the capacity bottleneck of modern wireless communications. In lieu of this demand, this project aims to develop a systematic approach to information transferring on the newly found statistic spectrum domain, which is based on the existence of cyclostationarity in wireless transmission signals. First of all, we will delve into the theoretical side and focus on modeling and capacity derivation of statistical signal transmission. Upper and lower bounds of the capacity will also be studied. Furthermore, to meet the challenge of fast fading channels in future wireless communication systems, we will investigate adaptive transceiver structures and algorithms for improved robustness of statistical signal transmission under such adverse environments. Moreover, considering the various complex scenarios in practical setup, we will look into the relationships between system performance and various design parameters such as number of antennas, number of subcarriers, length of observation window and etc. An optimized balancing problem will be studied in order to achieve optimal trade-off of algorithm complexity and detection performance. Our research will pave road for practical application of statistical signal transmission in future wireless communication systems.

新型信息传输维度的突破可以从根本上解决无线通信网络容量的瓶颈。面对这一重要需求，本项目将利用常规通信信号的高阶循环平稳特征为媒介，发展基于统计谱域的无线传输技术和理论体系，进一步探索新型维度的调制解调方式。首先，本项目将从理论角度分析统计谱域信号与其载体的内在联系，建立统计谱域的传输模型并推导其理论容量界。同时，面向未来移动通信中的快速时变信道场景，本项目将研究快速时变信道导致的统计特征异相化的本质原因，并针对性地提出基于异相位特征碎片捕捉的统计谱域信号检测算法。另外，考虑到实际应用中不同的无线传输场景会有不同的优化目标，本项目将深度探索在天线数量、子载波数目、以及观察窗长度等多参数的影响下，统计谱域传输技术在检测性能与算法复杂度之间的平衡优化问题，以引导统计谱域传输技术在实际场景中发挥最优的性能。

本研究发展了基于常规通信信号的高阶统计特征为媒介的新型信息传输理论，力求揭示统计谱域传输作为一种新型信息传输维度的内在原理和外部联系，为应对日益增长的无线通信需求提供可行的解决方案。针对快速时变信道导致的谱域信号检测性能下降问题，通过有效地识别和提取异相位上的特征碎片，力图克服统计谱域传输在快速时变信道环境下的局限性。与此同时，本研究着力寻求基于统计谱域传输理论在下一代通信场景中的潜在应用，顺应了通信技术的主流发展趋势，也使本研究的成果具有更广泛的推广和使用价值。